Mapping Action Units to Valence and Arousal Space Using Machine Learning

There are a lot of studies researching automated recognition of emotions. Emotions are represented as points in an emotion space. The emotion space itself is represented by different types of models. One is Facial Action Units System, another is Valence-Arousal-Dominance model. This study aims to create a mapping between these two emotion spaces. The data for the study was collected in a series of experiments with real humans, where both types of measurements were collected simultaneously. Given the data, we study the ability of machine learning models to create this type of mapping. We test different types of models against the task, such as tree-based models and linear models, and make conclusions about the optimal model.